Moore's Law posits that the number of transistors that can be inexpensively placed on an integrated circuit increases exponentially over time, doubling approximately every two years. Moore's Law was based upon an observation made by Intel co-founder Gordon E. Moore in a 1965 publication. This theory has held true for almost half a century of transistor technology development. This aggressive scaling of silicon technologies has led to transistors and many sensors becoming faster and smaller. The trend toward integrating sensors, interface circuits, and microprocessors into a single package or into a single chip is more and more prevalent. Although fabrication and packaging technologies enable an unprecedented number of components to be packed into a small volume, the accompanying power density can be higher than ever. This has become one of the bottle-neck factors in microsystem development. Conventionally, all classification, decision-making, or, in a more general term, information refinement tasks are performed in an digital processor.
For example, the typical conventional microsystem, receives analog inputs via sensors and passes the sensed signals to an analog to digital converter. Once the analog waveforms have been converted to digital signals, digital processing technology is then used to analyze the data, such as performing classification and data refinement. The analog-to-digital conversion of a broad range of analog inputs and the processing of a broad spectrum of data results in a large amount of power consumption by the analog-to-digital converter and the digital processor. In many conventional Microsystems, the digital processor may spend valuable processing time computing largely irrelevant analog input data.
If the information-refinement tasks could be performed in the analog domain with less power consumption, the specifications for the analog-to-digital-converters, which are usually power-hungry, can be relaxed. In some cases, depending upon the accuracy of the information-refinement, analog-to-digital conversion could be avoided altogether. Such systems could therefore significantly improve power efficiency over conventional systems.
Radial basis functions (“RBF”s) are widely used as the similarity measures in many recognition and classification applications. To efficiently realize the Gaussian or Gaussian-like radial basis functions in analog neural networks or classifiers, many analog RBF circuits have been utilized in the prior art. Among these previous works, the “bump” circuit in is the most classic because of its simplicity. The bump circuit is a small analog circuit for computing the similarity of two voltage inputs. The output current from the circuit becomes large when the two input voltages are close to each other and decreases exponentially when the input voltage difference increases. Thus, the output current from a bump circuit reaches a maximum value when the two input voltages are equal and the output current exponentially decreases when the voltage difference decreases. The transfer curve created by an analysis of the output current of a bump circuit is shaped like a Gaussian function; therefore, this simple circuit can be used to approximate a Gaussian function.
Although conventional RBF classifiers that implement a bump circuit to approximate the Gaussian function may work in certain limited applications, that are significantly inadequate in fully approximating the Gaussian function because these conventional classifiers cannot adjust the width of the transfer curve. Another drawback of these conventional devices is that they require extra hardware to store or to periodically refresh template data when they are employed in a recognition system. In view of these drawbacks, conventional analog classifiers are inaccurate and fail to provide statistic information that significantly reduces the amount of digital processing required. Most importantly, without the ability to approximate the variance of the Gaussian function, these conventional analog classifiers are insufficient.
Therefore, it would be advantageous to provide an apparatus and method for efficiently and accurately classifying analog signals.
Additionally, it would be advantageous to provide an apparatus and method to provide an analog classifier enabled to approximate the variance of the Gaussian function.
Additionally, it would be advantageous to provide an improved system and method for low power classification of analog data on the front end of a sensory device before digital processing is conducted.